Parametric Optimization of Medical Plastic Wastes Conversion into Transportation Fuel using Mamdani Fuzzy Inference Systems (FIS)
Amar Kumar Das1, Saroj Kumar Rout2, Dulari Hansdah3, Achyut Kumar Panda4
1Amar Kumar Das, Research Scholar, Department of Mechanical Engg, VSSUT Burla, Odisha.
2Saroj Kumar Rout, Department of CSE, GIFT, Bhubaneswar, Odisha.
3Dulari Hansdah, Department of Mechanical Engg, NIT Jamshedpur, India.
4Achyut Kumar Panda*, Department of Chemistry, Veer Surendra Sai University of Technology Burla, Odisha, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1438-1446 | Volume-8 Issue-6, March 2020. | Retrieval Number: D7135118419/2020©BEIESP | DOI: 10.35940/ijrte.D7135.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Rapid growth of medical plastic wastes required attention for its scientific disposal along with conversion into value added products. Pyrolysis method is found suitable process for such conversion of such wastes into liquid oil. The experiment was carried out with the medical plastic wastes collected from local medicals and treated in a batch reactor taking appropriate range of temperature change and use of Calcium bentonite (CB) and Zeolite-A (ZA) as catalysts. The yield of liquid oil, gas and char produced from the process are collected in scale. The yield of liquid fuel in this process was influenced by factors such as temperature, catalyst concentration and acidity of catalyst. It was observed that yield of liquid fuel in this process were significantly dependent on temperature, nature of catalyst and catalyst concentration. The maximum yield of oil reported at 500 C and even increased by adding 20% by weight of CB as catalyst and 10% by weight of Z-A. In this study, Mamdani Fuzzy inference System (FIS) is used in order to measure the performance of the process and can be analyzed with more objectives, oriented through mathematical modelling and simulation. Mamdani Fuzzy inference was also introduced to identify the significant factors affecting the response and helps to determine the best possible factor level of combination. Finally, a regression model for liquid fuel from catalytic degradation of medical plastic wastes has been developed and mapped as a function of process parameters.
Keywords: Medical Plastic Wastes, Thermo-Catalytic Degradation, Batch Reactor, Mamdani Fuzzy Inference System (FIS).
Scope of the Article: Design Optimization of Structures.